Training

This is a 14-hour course covering the principles of probabilistic modeling using Bayesian networks, building Bayesian networks based on expert knowledge (both structure and numerical parameters), dynamic Bayesian networks, learning Bayesian networks from data and causal discovery, parameter learning, validation techniques, elements of expected utility theory, utility elicitation, and influence diagrams.

 

Meeting times:

The course will take place through on-line meetings (Zoom).

9:00am-11:10am Eastern Time (6:00am-8:10am Pacific Time, 3:00pm-5:10pm Central European Time)

Monday, February 1, 2021

Tuesday, February 2, 2021

Wednesday, February 3, 2021

Thursday, February 4, 2021

Monday, February 8, 2021

Tuesday, February 9, 2021

Wednesday, February 10, 2021

 

Pre-requisites:

Elementary college-level math and computer skills, basic data processing skills through tools such as Excel.  No special prerequisites or knowledge of elements of decision-theoretic modeling or tools such as Bayesian networks.  We will cover all that is required in the course.  While all concepts covered in the course are general, we will use GeNIe to illustrate them.  Tuition covers a 30-day GeNIe license for use during the course.

 

Tuition fee:

Course tuition fee $500 ($300 for students)

There is a minimum of 5 and a maximum of 20 participants.

 

For more information/to register:

Contact training@bayesfusion.com

Training

This is a 12-hour course covering the principles of probabilistic modeling using Bayesian networks, building Bayesian networks based on expert knowledge (both structure and numerical parameters), dynamic Bayesian networks, learning Bayesian networks from data and causal discovery, parameter learning, validation techniques, elements of expected utility theory, utility elicitation, and influence diagrams.

 

Meeting times:

The course will take place through on-line meetings (Zoom).

1:00pm-3:10pm Eastern Time (10am-12:10pm Pacific Time)

Thursday, November 5, 2020

Friday, November 6, 2020

Monday, November 9, 2020

Tuesday, November 10, 2020

Thursday, November 12, 2020

Friday, November 13, 2020

 

Pre-requisites:

Elementary college-level math and computer skills, basic data processing skills through tools such as Excel.  No special prerequisites or knowledge of elements of decision-theoretic modeling or tools such as Bayesian networks.  We will cover all that is required in the course.  While all concepts covered in the course are general, we will use GeNIe to illustrate them.  Tuition covers a 30-day GeNIe license for use during the course.

 

Tuition fee:

Course tuition fee $500 ($300 for students)

There is a minimum of 5 and a maximum of 20 participants.

 

For more information/to register:

Contact training@bayesfusion.com

The Program Committee of the 10th Probabilistic Graphical Models (PGM 2020) conference announced the winner of the BayesFusion Best Student Paper Award in Aalborg, Denmark, on September 25. The winner is:

Alessandro Bregoli, Universita degli Studi di Milano-Bicocca, Milano, Italy, for the paper entitled Constraint-Based Learning for Continuous-Time Bayesian Networks, co-authored with Marco Scutari and Fabio Stella.

SMILE

BayesFusion releases SMILE 1.6. This version fully supports Unicode in node identifiers, names, and other textual attributes stored in models.

To download the library, visit https://download.bayesfusion.com

The documentation is available at https://support.bayesfusion.com/docs

 

 

 

We have just released GeNIe 3.0. This version includes a user interface refresh and full support for Unicode in directory paths and all texts inside models, so GeNIe 3.0 supports non-Latin alphabets.

To download the GeNIe Installer, visit https://download.bayesfusion.com

We have released GeNIe 2.5. This version includes QGeNIe, a qualitative interface to Bayesian networks useful for rapid prototyping and support of strategic decisions in group settings.

We have also introduced many small improvements that further enhance user modeling interface, including streamlining GeNIe diagnostic interface, various ways of selecting and highlighting model elements, and model navigation.

SMILE

BayesFusion releases SMILE 1.5. This release includes rSMILE, a native wrapper for R.

SMILE

SMILE 1.4 is now available. Notable new features in this release include:

  • full support for Python 3.x
  • new functions, random generators and custom functions for use in equation-based nodes
  • noisyMAX decomposition algorithm
  • specialized entropy-based diagnosis algorithm for mutually exclusive faults
  • performance and reliability improvements

Libraries are available for download now at https://download.bayesfusion.com.

Fun fact: during the full build process we currently compile 60 SMILE variants for use with Visual C++. 60 = 5 x 3 x 2 x 2.

  • 5 Visual Studio versions (2010, ’12, ’13, ’15, ’17)
  • 3 C++ runtime types (static release, DLL release, DLL debug)
  • 2 CPU architectures (x86 and x64)
  • 2 product editions (Academic and Business)

This of course is in addition to many versions for Linux, Mac, iOS and accompanying wrappers for multiple programming languages.

BayesFusion now offers free, 30-day, hosted evaluation of BayesBox. We maintain the server, the customer only needs to upload the networks using browser-based admin interface. Access to uploaded models can be protected by password on request.

Contact us for details.